With the proliferation of social media over the last decade, determining people’s attitude with respect to a specific topic, document, interaction or events has fueled research interest in natural language processing and introduced a new channel called “sentiment and emotion analysis” [1]. For instance, businesses routinely look to develop systems to automatically understand their customer conversations by identifying the relevant content to enhance marketing their products and managing their reputations [2]. Previous efforts to assess people’s sentiment on Twitter have suggested that Twitter may be a valuable resource for studying political sentiment and that it reflects the offline political landscape. According to a Pew Research Center report, in January 2016 44% of US adults stated having learned about the presidential election through social media. Furthermore, 24% reported use of social media posts of the two candidates as a source of news and information, which is more than the 15% who have used both candidates’ websites or emails combined (http://j.mp/PewSocM). The first presidential debate between Trump and Hillary was the most tweeted debate ever with 17.1 million tweets.